Spectral characteristics of sea snot reflectance observed from satellites: Implications for remote sensing of marine debris

Hu, Chuanmin; Qi, Lin; Xie, Yuyuan; Zhang, Shuai; Barnes, Brian B.

doi:10.1016/j.rse.2021.112842

Cited by 34 publications

(14 citation statements)

References 21 publications

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“…Therefore, the spectral reflectance dataset presented here is incomplete. The use of data from other similar pathfinders, for example the DLR Earth Sensing Imaging Spectrometer (DESIS) on the ISS (235 bands from 400-1000 nm, 30 m resolution, 2018-present) and the PRecursore IperSpettrale della Missione Applicativa (PRISMA, 237 bands from 400-2505 nm, 30 m resolution, 2019-present), may complement the spectral data using the same approach presented here (e.g., sea snot reflectance spectra derived from DESIS; Hu et al, 2022). Even in their present form, given the large variety of floating matters presented here, the spectral data may lead to several implications for current and future satellite missions.…”

Section: Implications For Current and Future Satellite Missionsmentioning

confidence: 94%

“…Because of improved spectral resolution and instrument sensitivity, mapping various types of floating matters has also become possible (IOCCG, 2014). These floating matters range from living to non-living, including Sargassum macroalgae, Ulva macroalgae, cyanobacterium Microcystis, cyanobacterium Trichodesmium, dinoflagellate Noctiluca, aquatic plants, brine shrimp cysts, oil slicks, pumice rafts, sea snot, and marine debris, among others (Qi et al, 2020;Hu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations

2022

Earth Syst. Sci. Data

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Abstract. Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station between 2010–2014, hyperspectral reflectance spectra of various floating matters in global oceans and lakes are derived for the spectral range of 400–800 nm. Specifically, the entire HICO archive of 9411 scenes is first visually inspected to identify suspicious image slicks. Then, a nearest-neighbor atmospheric correction is used to derive surface reflectance of slick pixels. Finally, a spectral unmixing scheme is used to derive the reflectance spectra of floating matters. Analysis of the spectral shapes of these various floating matters (macroalgae, microalgae, organic particles, whitecaps) through the use of a spectral angle mapper (SAM) index indicates that they can mostly be distinguished from each other without the need for ancillary information. Such reflectance spectra from the consistent 90 m resolution HICO observations are expected to provide spectral endmembers to differentiate and quantify the various floating matters from existing multi-band satellite sensors and future hyperspectral satellite missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) mission; and Surface Biology and Geology (SBG) mission. All spectral data are available at https://doi.org/10.21232/74LvC3Kr (Hu, 2021b).

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Section: Implications For Current and Future Satellite Missionsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations

2022

Earth Syst. Sci. Data

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“…The study examined the impact of mucilage on specific sectors such as ecology, fisheries, and tourism. Intense mucilage formation in the Sea of Marmara is being investigated by other studies (Balkis et al, 2021;Acar et al, 2021;Kavzoğlu et a., 2021;Hu et al, 2022). The authors imply that due to the excellent mucilage event, the habitat of the sea changed, and unobserved species became dominant.…”

Section: Introductionmentioning

confidence: 94%

Effects of Mucilage on Safety Navigation in the Turkish Straits

USLUER

2022

International Journal of Environment and Geoinformatics

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Mucilage, which has surrounded the Marmara Sea and the Turkish Straits in particular for the last few months, is a gel-like and slippery mass of microorganisms that are mostly clumped together and cover large areas, consisting of proteins, carbohydrates, and fatty acids released out of the body by single-celled organisms under stressful environmental conditions. Although the Turkish Straits sea area is the waterway connecting the Caspian Sea and the Mediterranean Sea, it is essential to transition for marine species. The mucilage effect on the filtration of the ship systems is caused by the seawater used by the ship's cooling systems during the passage through the straits. Due to the protection of local and global sea areas, a high level of attention is needed, especially on significant waterways located on the transit route of ships moving to many ports of the world serving global transportation such as the Turkish Straits. The study is trying to describe Mucilage's effects on ships during sailing at the Turkish Straits.

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“…1) Optical Images: Optical images are a kind of remote sensing data that obtains target information on different spectra by dividing the radiation of objects into several narrower spectral bands. The same objects have similar spectral characteristics [92]. The radiation energy of different objects in bands is different.…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Spectral characteristics of sea snot reflectance observed from satellites: Implications for remote sensing of marine debris

Cited by 34 publications

References 21 publications

Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations

Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations

Effects of Mucilage on Safety Navigation in the Turkish Straits

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

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